Overview

Dataset statistics

Number of variables16
Number of observations4262
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory532.9 KiB
Average record size in memory128.0 B

Variable types

Categorical1
Numeric15

Alerts

begin is highly correlated with endHigh correlation
end is highly correlated with beginHigh correlation
length_gene is highly correlated with per_geneHigh correlation
length_rpt is highly correlated with length_LINE and 6 other fieldsHigh correlation
length_LINE is highly correlated with length_rpt and 3 other fieldsHigh correlation
length_SINE is highly correlated with length_rpt and 4 other fieldsHigh correlation
length_DNA is highly correlated with per_DNA and 2 other fieldsHigh correlation
length_LTR is highly correlated with length_rpt and 3 other fieldsHigh correlation
per_DNA is highly correlated with length_DNA and 2 other fieldsHigh correlation
per_SINE is highly correlated with length_rpt and 6 other fieldsHigh correlation
per_LTR is highly correlated with length_rpt and 8 other fieldsHigh correlation
per_LINE is highly correlated with length_rpt and 6 other fieldsHigh correlation
per_length is highly correlated with length_rpt and 6 other fieldsHigh correlation
per_gene is highly correlated with length_geneHigh correlation
begin is highly correlated with endHigh correlation
end is highly correlated with beginHigh correlation
length_gene is highly correlated with per_geneHigh correlation
length_rpt is highly correlated with length_LINE and 5 other fieldsHigh correlation
length_LINE is highly correlated with length_rpt and 2 other fieldsHigh correlation
length_SINE is highly correlated with per_SINE and 1 other fieldsHigh correlation
length_DNA is highly correlated with per_DNA and 2 other fieldsHigh correlation
length_LTR is highly correlated with length_rpt and 3 other fieldsHigh correlation
per_DNA is highly correlated with length_DNA and 2 other fieldsHigh correlation
per_SINE is highly correlated with length_rpt and 4 other fieldsHigh correlation
per_LTR is highly correlated with length_rpt and 7 other fieldsHigh correlation
per_LINE is highly correlated with length_rpt and 4 other fieldsHigh correlation
per_length is highly correlated with length_rpt and 4 other fieldsHigh correlation
per_gene is highly correlated with length_geneHigh correlation
begin is highly correlated with end and 1 other fieldsHigh correlation
end is highly correlated with begin and 1 other fieldsHigh correlation
length_gene is highly correlated with block_len and 1 other fieldsHigh correlation
length_rpt is highly correlated with length_LINE and 4 other fieldsHigh correlation
length_LINE is highly correlated with length_rpt and 3 other fieldsHigh correlation
length_SINE is highly correlated with block_len and 2 other fieldsHigh correlation
length_DNA is highly correlated with block_len and 1 other fieldsHigh correlation
length_LTR is highly correlated with block_lenHigh correlation
block_len is highly correlated with begin and 7 other fieldsHigh correlation
per_DNA is highly correlated with length_DNAHigh correlation
per_SINE is highly correlated with length_SINE and 2 other fieldsHigh correlation
per_LTR is highly correlated with length_rpt and 4 other fieldsHigh correlation
per_LINE is highly correlated with length_rpt and 4 other fieldsHigh correlation
per_length is highly correlated with length_rpt and 3 other fieldsHigh correlation
per_gene is highly correlated with length_geneHigh correlation
chrom is highly correlated with length_rpt and 7 other fieldsHigh correlation
begin is highly correlated with endHigh correlation
end is highly correlated with beginHigh correlation
length_gene is highly correlated with per_geneHigh correlation
length_rpt is highly correlated with chrom and 7 other fieldsHigh correlation
length_LINE is highly correlated with chrom and 6 other fieldsHigh correlation
length_SINE is highly correlated with chrom and 6 other fieldsHigh correlation
length_DNA is highly correlated with per_DNA and 2 other fieldsHigh correlation
length_LTR is highly correlated with chrom and 5 other fieldsHigh correlation
per_DNA is highly correlated with length_DNA and 2 other fieldsHigh correlation
per_SINE is highly correlated with chrom and 8 other fieldsHigh correlation
per_LTR is highly correlated with chrom and 8 other fieldsHigh correlation
per_LINE is highly correlated with chrom and 7 other fieldsHigh correlation
per_length is highly correlated with chrom and 7 other fieldsHigh correlation
per_gene is highly correlated with length_geneHigh correlation
length_gene has 1012 (23.7%) zeros Zeros
length_LTR has 90 (2.1%) zeros Zeros
per_gene has 1012 (23.7%) zeros Zeros

Reproduction

Analysis started2022-02-23 12:49:16.171498
Analysis finished2022-02-23 12:49:59.679621
Duration43.51 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

chrom
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size33.4 KiB
LR999924.1
 
171
LR999925.1
 
167
LR999926.1
 
164
LR999927.1
 
161
LR999928.1
 
161
Other values (27)
3438 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLR999924.1
2nd rowLR999924.1
3rd rowLR999924.1
4th rowLR999924.1
5th rowLR999924.1

Common Values

ValueCountFrequency (%)
LR999924.1171
 
4.0%
LR999925.1167
 
3.9%
LR999926.1164
 
3.8%
LR999927.1161
 
3.8%
LR999928.1161
 
3.8%
LR999929.1160
 
3.8%
LR999930.1158
 
3.7%
LR999931.1156
 
3.7%
LR999932.1155
 
3.6%
LR999933.1153
 
3.6%
Other values (22)2656
62.3%

Length

2022-02-23T13:49:59.858586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lr999924.1171
 
4.0%
lr999925.1167
 
3.9%
lr999926.1164
 
3.8%
lr999927.1161
 
3.8%
lr999928.1161
 
3.8%
lr999929.1160
 
3.8%
lr999930.1158
 
3.7%
lr999931.1156
 
3.7%
lr999932.1155
 
3.6%
lr999933.1153
 
3.6%
Other values (22)2656
62.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

begin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct171
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6916096.73
Minimum1
Maximum17000001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:00.052512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600001
Q13300001
median6600001
Q310300001
95-th percentile14200001
Maximum17000001
Range17000000
Interquartile range (IQR)7000000

Descriptive statistics

Standard deviation4271589.444
Coefficient of variation (CV)0.6176300897
Kurtosis-1.002387132
Mean6916096.73
Median Absolute Deviation (MAD)3500000
Skewness0.2182060007
Sum2.947640426 × 1010
Variance1.824647637 × 1013
MonotonicityNot monotonic
2022-02-23T13:50:00.303330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132
 
0.8%
480000132
 
0.8%
340000132
 
0.8%
350000132
 
0.8%
360000132
 
0.8%
370000132
 
0.8%
380000132
 
0.8%
390000132
 
0.8%
400000132
 
0.8%
410000132
 
0.8%
Other values (161)3942
92.5%
ValueCountFrequency (%)
132
0.8%
10000132
0.8%
20000132
0.8%
30000132
0.8%
40000132
0.8%
50000132
0.8%
60000132
0.8%
70000132
0.8%
80000132
0.8%
90000132
0.8%
ValueCountFrequency (%)
170000011
 
< 0.1%
169000011
 
< 0.1%
168000011
 
< 0.1%
167000011
 
< 0.1%
166000012
< 0.1%
165000012
< 0.1%
164000012
< 0.1%
163000013
0.1%
162000013
0.1%
161000013
0.1%

end
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct202
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7015715.099
Minimum100000
Maximum17040296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:00.466151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile700000
Q13400000
median6700000
Q310400000
95-th percentile14300000
Maximum17040296
Range16940296
Interquartile range (IQR)7000000

Descriptive statistics

Standard deviation4270989.261
Coefficient of variation (CV)0.6087746154
Kurtosis-1.003096817
Mean7015715.099
Median Absolute Deviation (MAD)3500000
Skewness0.2178064853
Sum2.990097775 × 1010
Variance1.824134927 × 1013
MonotonicityNot monotonic
2022-02-23T13:50:00.689851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000032
 
0.8%
470000032
 
0.8%
340000032
 
0.8%
350000032
 
0.8%
360000032
 
0.8%
370000032
 
0.8%
380000032
 
0.8%
390000032
 
0.8%
400000032
 
0.8%
410000032
 
0.8%
Other values (192)3942
92.5%
ValueCountFrequency (%)
10000032
0.8%
20000032
0.8%
30000032
0.8%
40000032
0.8%
50000032
0.8%
60000032
0.8%
70000032
0.8%
80000032
0.8%
90000032
0.8%
100000032
0.8%
ValueCountFrequency (%)
170402961
< 0.1%
170000001
< 0.1%
169000001
< 0.1%
168000001
< 0.1%
167000001
< 0.1%
166070081
< 0.1%
166000002
< 0.1%
165000002
< 0.1%
164000002
< 0.1%
163620691
< 0.1%

length_gene
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2821
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3703.10488
Minimum0
Maximum26501
Zeros1012
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:00.930199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1202.25
median2201.5
Q35627.5
95-th percentile12723.3
Maximum26501
Range26501
Interquartile range (IQR)5425.25

Descriptive statistics

Standard deviation4351.954525
Coefficient of variation (CV)1.175217734
Kurtosis3.037885145
Mean3703.10488
Median Absolute Deviation (MAD)2201.5
Skewness1.658515686
Sum15782633
Variance18939508.19
MonotonicityNot monotonic
2022-02-23T13:50:01.143915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01012
 
23.7%
11645
 
0.1%
6354
 
0.1%
23244
 
0.1%
4884
 
0.1%
11804
 
0.1%
3814
 
0.1%
17024
 
0.1%
22744
 
0.1%
6834
 
0.1%
Other values (2811)3213
75.4%
ValueCountFrequency (%)
01012
23.7%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
151
 
< 0.1%
181
 
< 0.1%
201
 
< 0.1%
212
 
< 0.1%
221
 
< 0.1%
ValueCountFrequency (%)
265011
< 0.1%
261571
< 0.1%
260381
< 0.1%
257941
< 0.1%
256011
< 0.1%
244301
< 0.1%
243761
< 0.1%
241261
< 0.1%
238491
< 0.1%
238451
< 0.1%

length_rpt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4083
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37262.81065
Minimum0
Maximum96531
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:01.487756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17122.9
Q127134.25
median36186.5
Q345876.75
95-th percentile62368.35
Maximum96531
Range96531
Interquartile range (IQR)18742.5

Descriptive statistics

Standard deviation13751.65069
Coefficient of variation (CV)0.3690449122
Kurtosis0.2351532905
Mean37262.81065
Median Absolute Deviation (MAD)9365
Skewness0.5243842441
Sum158814099
Variance189107896.6
MonotonicityNot monotonic
2022-02-23T13:50:01.727040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
435453
 
0.1%
280993
 
0.1%
376233
 
0.1%
422043
 
0.1%
299773
 
0.1%
426203
 
0.1%
406853
 
0.1%
244162
 
< 0.1%
218772
 
< 0.1%
276132
 
< 0.1%
Other values (4073)4235
99.4%
ValueCountFrequency (%)
01
< 0.1%
7421
< 0.1%
14881
< 0.1%
49451
< 0.1%
51201
< 0.1%
54031
< 0.1%
58761
< 0.1%
61901
< 0.1%
61941
< 0.1%
65071
< 0.1%
ValueCountFrequency (%)
965311
< 0.1%
948111
< 0.1%
911181
< 0.1%
888071
< 0.1%
877951
< 0.1%
861351
< 0.1%
836781
< 0.1%
834631
< 0.1%
822601
< 0.1%
820741
< 0.1%

length_LINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3885
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14882.06898
Minimum0
Maximum91264
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:01.977978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5002.8
Q19295.5
median13447
Q318548.75
95-th percentile28284.85
Maximum91264
Range91264
Interquartile range (IQR)9253.25

Descriptive statistics

Standard deviation8604.772486
Coefficient of variation (CV)0.5781973257
Kurtosis10.77237867
Mean14882.06898
Median Absolute Deviation (MAD)4554.5
Skewness2.375428347
Sum63427378
Variance74042109.54
MonotonicityNot monotonic
2022-02-23T13:50:02.194459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186654
 
0.1%
134513
 
0.1%
228183
 
0.1%
125173
 
0.1%
149353
 
0.1%
104943
 
0.1%
219963
 
0.1%
109813
 
0.1%
136413
 
0.1%
167143
 
0.1%
Other values (3875)4231
99.3%
ValueCountFrequency (%)
01
< 0.1%
3201
< 0.1%
7421
< 0.1%
8721
< 0.1%
9381
< 0.1%
13371
< 0.1%
13721
< 0.1%
15371
< 0.1%
16021
< 0.1%
16891
< 0.1%
ValueCountFrequency (%)
912641
< 0.1%
862601
< 0.1%
848161
< 0.1%
816871
< 0.1%
815331
< 0.1%
796331
< 0.1%
742281
< 0.1%
673211
< 0.1%
663201
< 0.1%
663151
< 0.1%

length_SINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3546
Distinct (%)83.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7259.453884
Minimum0
Maximum19106
Zeros24
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:02.445180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1528
Q14864
median7279
Q39607
95-th percentile12813
Maximum19106
Range19106
Interquartile range (IQR)4743

Descriptive statistics

Standard deviation3353.091747
Coefficient of variation (CV)0.4618931121
Kurtosis-0.4210047468
Mean7259.453884
Median Absolute Deviation (MAD)2375
Skewness0.04376935262
Sum30932533
Variance11243224.26
MonotonicityNot monotonic
2022-02-23T13:50:02.667485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
0.6%
78184
 
0.1%
48774
 
0.1%
118004
 
0.1%
98674
 
0.1%
74724
 
0.1%
85444
 
0.1%
49014
 
0.1%
44523
 
0.1%
84563
 
0.1%
Other values (3536)4203
98.6%
ValueCountFrequency (%)
024
0.6%
301
 
< 0.1%
381
 
< 0.1%
541
 
< 0.1%
551
 
< 0.1%
741
 
< 0.1%
871
 
< 0.1%
961
 
< 0.1%
1041
 
< 0.1%
1111
 
< 0.1%
ValueCountFrequency (%)
191061
< 0.1%
174711
< 0.1%
172281
< 0.1%
170891
< 0.1%
165231
< 0.1%
165181
< 0.1%
164851
< 0.1%
163541
< 0.1%
163171
< 0.1%
160061
< 0.1%

length_DNA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3007
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3009.844205
Minimum0
Maximum28268
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:02.894794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile645
Q11648.25
median2596
Q33822
95-th percentile6507.9
Maximum28268
Range28268
Interquartile range (IQR)2173.75

Descriptive statistics

Standard deviation2139.228929
Coefficient of variation (CV)0.710744073
Kurtosis17.85873945
Mean3009.844205
Median Absolute Deviation (MAD)1064
Skewness2.857204654
Sum12827956
Variance4576300.41
MonotonicityNot monotonic
2022-02-23T13:50:03.112623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014
 
0.3%
24987
 
0.2%
15646
 
0.1%
22356
 
0.1%
28115
 
0.1%
30775
 
0.1%
22245
 
0.1%
21495
 
0.1%
31775
 
0.1%
14414
 
0.1%
Other values (2997)4200
98.5%
ValueCountFrequency (%)
014
0.3%
111
 
< 0.1%
451
 
< 0.1%
503
 
0.1%
521
 
< 0.1%
641
 
< 0.1%
681
 
< 0.1%
722
 
< 0.1%
761
 
< 0.1%
771
 
< 0.1%
ValueCountFrequency (%)
282681
< 0.1%
281681
< 0.1%
216901
< 0.1%
212901
< 0.1%
186991
< 0.1%
176061
< 0.1%
170901
< 0.1%
169051
< 0.1%
167091
< 0.1%
164351
< 0.1%

length_LTR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2580
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2489.054904
Minimum0
Maximum30612
Zeros90
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:03.332108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1493
median1021.5
Q32698
95-th percentile9890.4
Maximum30612
Range30612
Interquartile range (IQR)2205

Descriptive statistics

Standard deviation3667.208483
Coefficient of variation (CV)1.473333705
Kurtosis11.03197296
Mean2489.054904
Median Absolute Deviation (MAD)665.5
Skewness2.957214714
Sum10608352
Variance13448418.06
MonotonicityNot monotonic
2022-02-23T13:50:03.563659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
090
 
2.1%
4718
 
0.2%
3018
 
0.2%
4317
 
0.2%
3857
 
0.2%
1057
 
0.2%
5507
 
0.2%
1657
 
0.2%
3657
 
0.2%
4756
 
0.1%
Other values (2570)4108
96.4%
ValueCountFrequency (%)
090
2.1%
101
 
< 0.1%
121
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
181
 
< 0.1%
192
 
< 0.1%
203
 
0.1%
222
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
306121
< 0.1%
299991
< 0.1%
283371
< 0.1%
271961
< 0.1%
269481
< 0.1%
265891
< 0.1%
262571
< 0.1%
252811
< 0.1%
251251
< 0.1%
247871
< 0.1%

block_len
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99618.36931
Minimum790
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:03.749869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum790
5-th percentile99999
Q199999
median99999
Q399999
95-th percentile99999
Maximum99999
Range99209
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5054.936551
Coefficient of variation (CV)0.05074301643
Kurtosis237.3472155
Mean99618.36931
Median Absolute Deviation (MAD)0
Skewness-14.93793537
Sum424573490
Variance25552383.53
MonotonicityNot monotonic
2022-02-23T13:50:03.983118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
999994230
99.2%
192971
 
< 0.1%
792961
 
< 0.1%
784671
 
< 0.1%
623681
 
< 0.1%
399331
 
< 0.1%
601361
 
< 0.1%
992601
 
< 0.1%
267821
 
< 0.1%
40861
 
< 0.1%
Other values (23)23
 
0.5%
ValueCountFrequency (%)
7901
< 0.1%
40861
< 0.1%
54051
< 0.1%
70071
< 0.1%
152121
< 0.1%
159981
< 0.1%
163821
< 0.1%
192971
< 0.1%
267601
< 0.1%
267821
< 0.1%
ValueCountFrequency (%)
999994230
99.2%
992601
 
< 0.1%
965601
 
< 0.1%
946731
 
< 0.1%
905501
 
< 0.1%
792961
 
< 0.1%
784671
 
< 0.1%
743681
 
< 0.1%
740351
 
< 0.1%
725231
 
< 0.1%

per_DNA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3015
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.017447978
Minimum0
Maximum28.26828268
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:04.171833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6520565206
Q11.656266563
median2.59802598
Q33.824788248
95-th percentile6.536765368
Maximum28.26828268
Range28.26828268
Interquartile range (IQR)2.168521685

Descriptive statistics

Standard deviation2.143907978
Coefficient of variation (CV)0.7105037081
Kurtosis17.71547502
Mean3.017447978
Median Absolute Deviation (MAD)1.064510645
Skewness2.852493441
Sum12860.36328
Variance4.596341417
MonotonicityNot monotonic
2022-02-23T13:50:04.414280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014
 
0.3%
2.498024987
 
0.2%
1.564015646
 
0.1%
2.235022356
 
0.1%
3.077030775
 
0.1%
2.811028115
 
0.1%
2.224022245
 
0.1%
3.177031775
 
0.1%
2.149021495
 
0.1%
2.317023174
 
0.1%
Other values (3005)4200
98.5%
ValueCountFrequency (%)
014
0.3%
0.011000111
 
< 0.1%
0.045000451
 
< 0.1%
0.050000500012
 
< 0.1%
0.052000520011
 
< 0.1%
0.064000640011
 
< 0.1%
0.068000680011
 
< 0.1%
0.072000720012
 
< 0.1%
0.076000760011
 
< 0.1%
0.077000770011
 
< 0.1%
ValueCountFrequency (%)
28.268282681
< 0.1%
28.168281681
< 0.1%
21.69021691
< 0.1%
21.29021291
< 0.1%
18.699186991
< 0.1%
17.606176061
< 0.1%
17.09017091
< 0.1%
16.905169051
< 0.1%
16.709167091
< 0.1%
16.435164351
< 0.1%

per_SINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3717
Distinct (%)87.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.28151654
Minimum0
Maximum28.61428614
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:04.712395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.81103811
Q17.268072681
median10.04410044
Q313.10713107
95-th percentile17.53117531
Maximum28.61428614
Range28.61428614
Interquartile range (IQR)5.839058391

Descriptive statistics

Standard deviation4.242424275
Coefficient of variation (CV)0.4126263143
Kurtosis0.1627838444
Mean10.28151654
Median Absolute Deviation (MAD)2.88302883
Skewness0.3464568187
Sum43809.54198
Variance17.99816373
MonotonicityNot monotonic
2022-02-23T13:50:04.932546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
0.2%
8.6250862514
 
0.1%
13.192131924
 
0.1%
8.9000890014
 
0.1%
11.735117354
 
0.1%
7.6260762613
 
0.1%
10.555105553
 
0.1%
11.58011583
 
0.1%
10.881108813
 
0.1%
10.844108443
 
0.1%
Other values (3707)4221
99.0%
ValueCountFrequency (%)
09
0.2%
0.050000500011
 
< 0.1%
0.072000720011
 
< 0.1%
0.175001751
 
< 0.1%
0.18669255471
 
< 0.1%
0.241002411
 
< 0.1%
0.25000251
 
< 0.1%
0.345003451
 
< 0.1%
0.412004121
 
< 0.1%
0.482004821
 
< 0.1%
ValueCountFrequency (%)
28.614286141
< 0.1%
28.332283321
< 0.1%
27.327273271
< 0.1%
27.302273021
< 0.1%
26.873268731
< 0.1%
25.274252741
< 0.1%
25.001250011
< 0.1%
24.984249841
< 0.1%
24.71024711
< 0.1%
24.525245251
< 0.1%

per_LTR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3835
Distinct (%)90.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.80545063
Minimum0
Maximum60.36379547
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:05.169315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.167051671
Q18.966089661
median12.17312173
Q316.02416024
95-th percentile22.27622276
Maximum60.36379547
Range60.36379547
Interquartile range (IQR)7.058070581

Descriptive statistics

Standard deviation5.39325079
Coefficient of variation (CV)0.4211683716
Kurtosis1.969924734
Mean12.80545063
Median Absolute Deviation (MAD)3.49103491
Skewness0.7896843695
Sum54564.02514
Variance29.08715408
MonotonicityNot monotonic
2022-02-23T13:50:05.375448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
0.1%
10.542105424
 
0.1%
10.107101074
 
0.1%
10.394103944
 
0.1%
11.886118864
 
0.1%
11.747117473
 
0.1%
10.682106823
 
0.1%
9.3540935413
 
0.1%
9.2180921813
 
0.1%
10.693106933
 
0.1%
Other values (3825)4226
99.2%
ValueCountFrequency (%)
04
0.1%
0.072000720011
 
< 0.1%
0.094000940011
 
< 0.1%
0.103001031
 
< 0.1%
0.18669255471
 
< 0.1%
0.241002411
 
< 0.1%
0.262002621
 
< 0.1%
0.46578018181
 
< 0.1%
0.60700607011
 
< 0.1%
1.109011091
 
< 0.1%
ValueCountFrequency (%)
60.363795471
< 0.1%
37.947379471
< 0.1%
37.237372371
< 0.1%
35.101351011
< 0.1%
34.95034951
< 0.1%
34.613346131
< 0.1%
33.943339431
< 0.1%
33.756337561
< 0.1%
33.685336851
< 0.1%
33.164331641
< 0.1%

per_LINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4039
Distinct (%)94.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean27.78898948
Minimum0
Maximum92.24450955
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:05.612607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.61411614
Q119.27519275
median26.39826398
Q334.63734637
95-th percentile48.68648686
Maximum92.24450955
Range92.24450955
Interquartile range (IQR)15.36215362

Descriptive statistics

Standard deviation12.03590237
Coefficient of variation (CV)0.4331176697
Kurtosis2.025386464
Mean27.78898948
Median Absolute Deviation (MAD)7.567075671
Skewness1.037073135
Sum118408.8842
Variance144.8629458
MonotonicityNot monotonic
2022-02-23T13:50:05.814774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.861238613
 
0.1%
14.218142183
 
0.1%
28.86028863
 
0.1%
20.53020533
 
0.1%
28.925289253
 
0.1%
17.737177373
 
0.1%
26.784267843
 
0.1%
21.925219253
 
0.1%
35.122351223
 
0.1%
32.416324163
 
0.1%
Other values (4029)4231
99.3%
ValueCountFrequency (%)
01
< 0.1%
2.047020471
< 0.1%
2.69002691
< 0.1%
3.373033731
< 0.1%
3.566035661
< 0.1%
4.068040681
< 0.1%
4.147041471
< 0.1%
4.252042521
< 0.1%
4.427044271
< 0.1%
4.497044971
< 0.1%
ValueCountFrequency (%)
92.244509551
< 0.1%
91.505915061
< 0.1%
88.34023121
< 0.1%
86.648866491
< 0.1%
86.363863641
< 0.1%
84.910849111
< 0.1%
82.294822951
< 0.1%
81.795817961
< 0.1%
78.258782591
< 0.1%
76.753767541
< 0.1%

per_length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4087
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.44999224
Minimum0
Maximum96.85974006
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:06.097975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.32922329
Q127.26452265
median36.31636316
Q346.08146081
95-th percentile62.6498265
Maximum96.85974006
Range96.85974006
Interquartile range (IQR)18.81693817

Descriptive statistics

Standard deviation13.80270056
Coefficient of variation (CV)0.3685635091
Kurtosis0.3122330378
Mean37.44999224
Median Absolute Deviation (MAD)9.364593646
Skewness0.5650704772
Sum159611.8669
Variance190.5145427
MonotonicityNot monotonic
2022-02-23T13:50:06.358470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.204422043
 
0.1%
37.623376233
 
0.1%
28.099280993
 
0.1%
40.685406853
 
0.1%
42.62042623
 
0.1%
29.977299773
 
0.1%
43.545435453
 
0.1%
26.14026142
 
< 0.1%
58.965589662
 
< 0.1%
51.067510682
 
< 0.1%
Other values (4077)4235
99.4%
ValueCountFrequency (%)
01
< 0.1%
4.945049451
< 0.1%
5.1200512011
< 0.1%
5.8760587611
< 0.1%
6.1940619411
< 0.1%
6.5070650711
< 0.1%
6.6910669111
< 0.1%
6.8980689811
< 0.1%
7.8570785711
< 0.1%
8.1300813011
< 0.1%
ValueCountFrequency (%)
96.859740061
< 0.1%
96.531965321
< 0.1%
94.811948121
< 0.1%
91.118911191
< 0.1%
88.807888081
< 0.1%
88.34023121
< 0.1%
87.795877961
< 0.1%
86.135861361
< 0.1%
83.678836791
< 0.1%
83.463834641
< 0.1%

per_gene
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2828
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.714060184
Minimum0
Maximum26.50126501
Zeros1012
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-02-23T13:50:06.602081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.20300203
median2.20902209
Q35.653556536
95-th percentile12.73742737
Maximum26.50126501
Range26.50126501
Interquartile range (IQR)5.450554506

Descriptive statistics

Standard deviation4.358827368
Coefficient of variation (CV)1.173601706
Kurtosis2.99931233
Mean3.714060184
Median Absolute Deviation (MAD)2.20902209
Skewness1.650159454
Sum15829.3245
Variance18.99937602
MonotonicityNot monotonic
2022-02-23T13:50:06.811134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01012
 
23.7%
1.164011645
 
0.1%
0.488004884
 
0.1%
0.381003814
 
0.1%
1.702017024
 
0.1%
1.18001184
 
0.1%
2.324023244
 
0.1%
0.63500635014
 
0.1%
0.68300683014
 
0.1%
2.274022744
 
0.1%
Other values (2818)3213
75.4%
ValueCountFrequency (%)
01012
23.7%
0.0090000900011
 
< 0.1%
0.01000011
 
< 0.1%
0.011000111
 
< 0.1%
0.012000122
 
< 0.1%
0.015000151
 
< 0.1%
0.018000181
 
< 0.1%
0.02000021
 
< 0.1%
0.021000212
 
< 0.1%
0.022000221
 
< 0.1%
ValueCountFrequency (%)
26.501265011
< 0.1%
26.157261571
< 0.1%
26.038260381
< 0.1%
25.794257941
< 0.1%
25.601256011
< 0.1%
24.43024431
< 0.1%
24.376243761
< 0.1%
24.126241261
< 0.1%
23.849238491
< 0.1%
23.845238451
< 0.1%

Interactions

2022-02-23T13:49:55.617958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:21.479309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:23.941594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:26.417904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:28.782594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:31.020026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:33.307094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:35.772418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:38.146472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:40.662972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:43.363152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:45.940880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:48.448631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:50.839408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:53.172998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:55.761212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:21.691085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:24.235120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:26.608880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:28.943730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:31.272683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:33.468368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:35.925414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:38.309141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:40.818697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:43.524987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:46.117064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:48.592186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:50.994533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:53.366601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:55.968085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:21.882130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:24.377771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:26.757095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:29.089131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:31.405516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:33.647311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:36.176246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:38.586377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:40.987515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:43.717530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:46.369621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:48.737517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:51.190832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:53.532959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:56.108749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:22.064963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:24.551121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:26.906855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:29.230708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:31.537872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:36.344001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:38.716856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:41.156497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:43.858118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:46.649826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:48.867310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:51.344194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:53.675238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:56.270659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:27.056769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:31.678779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:33.944443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:36.498169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:38.880319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:41.314106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:44.018387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:46.777379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:49.048679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:51.496506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:53.949175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:56.429278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:22.431661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:24.868591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:27.194447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:29.529348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:31.819913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:34.101553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:36.655200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:39.013815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:41.481733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:44.176502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:46.956955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:49.186238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:51.627971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:54.100796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:56.583233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:22.596549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:25.057396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:27.345629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:29.703197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:31.977886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:34.263957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:36.819043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:39.170705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:41.668269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:47.119106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:51.814387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:56.904085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:39.978284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-23T13:49:50.682310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:53.009389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-23T13:49:55.473438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-02-23T13:50:07.076690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-23T13:50:07.312844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-23T13:50:07.547552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-23T13:50:07.902192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-23T13:49:58.310062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-23T13:49:58.836194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-23T13:49:59.233394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-23T13:49:59.447530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

chrombeginendlength_genelength_rptlength_LINElength_SINElength_DNAlength_LTRblock_lenper_DNAper_SINEper_LTRper_LINEper_lengthper_gene
0LR999924.11100000680787795.081533.00.050212.0999990.0500010.0500010.26200381.79581887.7958786.807068
1LR999924.11000012000001263434898.016750.02711.040851215.0999994.0850416.7960688.01108024.76124834.89834912.634126
2LR999924.12000013000002579410577.03534.01514.0809601.0999990.8090082.3230232.9240296.45806510.57710625.794258
3LR999924.1300001400000268315688.06128.03287.02835105.0999992.8350286.1220616.22706212.35512415.6881572.683027
4LR999924.1400001500000734313046.04109.02529.014810.0999991.4810154.0100404.0100408.11908113.0461307.343073
5LR999924.1500001600000670616456.05859.04461.0293357.0999990.2930034.7540485.11105110.97011016.4561656.706067
6LR999924.1600001700000024316.010325.05439.02210798.0999992.2100227.6490768.44708418.77218824.3162430.000000
7LR999924.17000018000001025618682.06959.03697.016950.0999991.6950175.3920545.39205412.35112418.68218710.256103
8LR999924.1800001900000563822796.06732.04262.032591122.0999993.2590337.5210758.64308615.37515422.7962285.638056
9LR999924.19000011000000253227620.07654.06879.04290686.0999994.29004311.16911211.85511919.50919527.6202762.532025

Last rows

chrombeginendlength_genelength_rptlength_LINElength_SINElength_DNAlength_LTRblock_lenper_DNAper_SINEper_LTRper_LINEper_lengthper_gene
4252LR999955.152000015300000256954826.018955.011045.065061261.0999996.50606517.55117618.81218837.76737854.8265482.569026
4253LR999955.153000015400000870758502.017374.08331.068429333.0999996.84206815.17315224.50624541.88041958.5025858.707087
4254LR999955.154000015500000293358354.021659.09106.062642985.0999996.26406315.37015418.35518440.01440058.3545842.933029
4255LR999955.155000015600000192143907.015371.011011.02563800.0999992.56302613.57413614.37414429.74529743.9074391.921019
4256LR999955.156000015700000863854464.016233.010521.062613831.0999996.26106316.78216820.61320636.84636854.4645458.638086
4257LR999955.157000015800000110769323.032035.010528.051307569.0999995.13005115.65815723.22723255.26255369.3236931.107011
4258LR999955.158000015900000302969397.021158.09324.018768124.0999991.87601911.20011219.32419340.48240569.3976943.029030
4259LR999955.15900001600000016466234.021179.0746.0467911839.0999994.6790475.42505417.26417338.44338466.2346620.164002
4260LR999955.160000016100000051067.017755.02374.021018154.0999992.1010214.47504512.62912630.38430451.0675110.000000
4261LR999955.16100001616633416442720.013392.0267.027789509.0663334.1879614.59047518.92572339.11476964.4023340.247237